用深度模型从短文本预测表情符号，提升人机交互的语义理解。
Understanding Textual Emotion Through Emoji Prediction
- 对比四种神经网络架构，评估其表情符号预测能力
- BERT整体表现最优，CNN在罕见表情符号上更优
- 适合关注情感计算与自然语言生成的研究者
本研究通过四种深度学习架构——前馈网络、CNN、Transformer和BERT，探索从短文本序列中预测表情符号。基于TweetEval数据集，采用焦点损失和正则化技术缓解类别不平衡问题。结果表明，由于预训练优势，BERT在整体性能上表现最佳；而CNN在罕见表情符号类别上展现出更高效率。该研究强调了模型架构选择与超参数调优对情感感知表情符号预测的重要性，有助于提升人机交互中的语义理解能力。
原文摘要 · Abstract (English)
This project explores emoji prediction from short text sequences using four deep learning architectures: a feed-forward network, CNN, transformer, and BERT. Using the TweetEval dataset, we address class imbalance through focal loss and regularization techniques. Results show BERT achieves the highest overall performance due to its pre-training advantage, while CNN demonstrates superior efficacy on rare emoji classes. This research shows the importance of architecture selection and hyperparameter tuning for sentiment-aware emoji prediction, contributing to improved human-computer interaction.
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